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        "\n# Defining a custom corpus for plotting text\n\nBy default, the text samples will be transformed into a vector of word counts\nand then modeled using Latent Dirichlet Allocation (# of topics = 100) using a\nmodel fit to a large sample of wikipedia pages.  However, you can optionally\npass your own text to fit the semantic model. To do this define corpus as a\nlist of documents (strings). A topic model will be fit on the fly and the text\nwill be plotted.\n"
      ]
    },
    {
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      "source": [
        "# Code source: Andrew Heusser\n# License: MIT\n\n# load hypertools\nimport hypertools as hyp\n\n# load the data\ntext_samples = ['i like cats alot', 'cats r pretty cool', 'cats are better than dogs',\n        'dogs rule the haus', 'dogs are my jam', 'dogs are a mans best friend',\n        'i haz a cheezeburger?']\n\n# plot it\nhyp.plot(text_samples, 'o', corpus=text_samples)"
      ]
    }
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